{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "6.BurgersEquation(Inverse).ipynb",
      "provenance": [],
      "collapsed_sections": [
        "i0j73GoH86-m"
      ],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jdtoscano94/Learning-PINNs-in-Python-Physics-Informed-Machine-Learning/blob/main/6_BurgersEquation(Inverse).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YE6L1P-D5DzF",
        "outputId": "8d12c7e0-1a0a-476d-c161-3240321d2274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
        "id": "Y61YA90-WIZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.autograd as autograd         # computation graph\n",
        "from torch import Tensor                  # tensor node in the computation graph\n",
        "import torch.nn as nn                     # neural networks\n",
        "import torch.optim as optim               # optimizers e.g. gradient descent, ADAM, etc.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import matplotlib.ticker\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import numpy as np\n",
        "import time\n",
        "import scipy.io\n",
        "\n",
        "#Set default dtype to float32\n",
        "torch.set_default_dtype(torch.float)\n",
        "\n",
        "#PyTorch random number generator\n",
        "torch.manual_seed(1234)\n",
        "\n",
        "# Random number generators in other libraries\n",
        "np.random.seed(1234)\n",
        "\n",
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "print(device)\n",
        "\n",
        "if device == 'cuda': \n",
        "    print(torch.cuda.get_device_name()) \n"
      ],
      "metadata": {
        "id": "OdYMzuSDi7Js",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "115a59cf-1bdc-433b-e506-c8dc625a6ba7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tunning Parameters"
      ],
      "metadata": {
        "id": "UoqCzgLSp61M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "steps=20000\n",
        "lr=1e-1\n",
        "layers = np.array([2,20,20,20,20,20,20,20,20,1]) #8 hidden layers\n",
        "N_u = 100 #Total number of data points for 'u'\n",
        "N_f = 10_000 #Total number of collocation points \n",
        "nu = 0.01/np.pi #diffusion coefficient"
      ],
      "metadata": {
        "id": "L2r-CxAnp5Ub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Auxiliary Functions\n"
      ],
      "metadata": {
        "id": "i0j73GoH86-m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot3D(x,t,y):\n",
        "  x_plot =x.squeeze(1) \n",
        "  t_plot =t.squeeze(1)\n",
        "  X,T= torch.meshgrid(x_plot,t_plot)\n",
        "  F_xt = y\n",
        "  fig,ax=plt.subplots(1,1)\n",
        "  cp = ax.contourf(T,X, F_xt,20,cmap=\"rainbow\")\n",
        "  fig.colorbar(cp) # Add a colorbar to a plot\n",
        "  ax.set_title('F(x,t)')\n",
        "  ax.set_xlabel('t')\n",
        "  ax.set_ylabel('x')\n",
        "  plt.show()\n",
        "  ax = plt.axes(projection='3d')\n",
        "  ax.plot_surface(T.numpy(), X.numpy(), F_xt.numpy(),cmap=\"rainbow\")\n",
        "  ax.set_xlabel('t')\n",
        "  ax.set_ylabel('x')\n",
        "  ax.set_zlabel('f(x,t)')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "F8Y6q7m288zJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot3D_Matrix(x,t,y):\n",
        "  X,T= x,t\n",
        "  F_xt = y\n",
        "  fig,ax=plt.subplots(1,1)\n",
        "  cp = ax.contourf(T,X, F_xt,20,cmap=\"rainbow\")\n",
        "  fig.colorbar(cp) # Add a colorbar to a plot\n",
        "  ax.set_title('F(x,t)')\n",
        "  ax.set_xlabel('t')\n",
        "  ax.set_ylabel('x')\n",
        "  plt.show()\n",
        "  ax = plt.axes(projection='3d')\n",
        "  ax.plot_surface(T.numpy(), X.numpy(), F_xt.numpy(),cmap=\"rainbow\")\n",
        "  ax.set_xlabel('t')\n",
        "  ax.set_ylabel('x')\n",
        "  ax.set_zlabel('f(x,t)')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "pFS1eS92dhuX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def solutionplot(u_pred,X_u_train,u_train):\n",
        "  #https://github.com/omniscientoctopus/Physics-Informed-Neural-Networks\n",
        "    \n",
        "    fig, ax = plt.subplots()\n",
        "    ax.axis('off')\n",
        "\n",
        "    gs0 = gridspec.GridSpec(1, 2)\n",
        "    gs0.update(top=1-0.06, bottom=1-1/3, left=0.15, right=0.85, wspace=0)\n",
        "    ax = plt.subplot(gs0[:, :])\n",
        "\n",
        "    h = ax.imshow(u_pred, interpolation='nearest', cmap='rainbow', \n",
        "                extent=[T.min(), T.max(), X.min(), X.max()], \n",
        "                origin='lower', aspect='auto')\n",
        "    divider = make_axes_locatable(ax)\n",
        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
        "    fig.colorbar(h, cax=cax)\n",
        "    \n",
        "    ax.plot(X_u_train[:,1], X_u_train[:,0], 'kx', label = 'Data (%d points)' % (u_train.shape[0]), markersize = 0.5, clip_on = False)\n",
        "\n",
        "    line = np.linspace(x.min(), x.max(), 2)[:,None]\n",
        "    ax.plot(t[25]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
        "    ax.plot(t[50]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
        "    ax.plot(t[75]*np.ones((2,1)), line, 'w-', linewidth = 1)    \n",
        "\n",
        "    ax.set_xlabel('$t$')\n",
        "    ax.set_ylabel('$x$')\n",
        "    ax.legend(frameon=False, loc = 'best')\n",
        "    ax.set_title('$u(x,t)$', fontsize = 10)\n",
        "    \n",
        "    ''' \n",
        "    Slices of the solution at points t = 0.25, t = 0.50 and t = 0.75\n",
        "    '''\n",
        "    \n",
        "    ####### Row 1: u(t,x) slices ##################\n",
        "    gs1 = gridspec.GridSpec(1, 3)\n",
        "    gs1.update(top=1-1/3, bottom=0, left=0.1, right=0.9, wspace=0.5)\n",
        "\n",
        "    ax = plt.subplot(gs1[0, 0])\n",
        "    ax.plot(x,usol.T[25,:], 'b-', linewidth = 2, label = 'Exact')       \n",
        "    ax.plot(x,u_pred.T[25,:], 'r--', linewidth = 2, label = 'Prediction')\n",
        "    ax.set_xlabel('$x$')\n",
        "    ax.set_ylabel('$u(x,t)$')    \n",
        "    ax.set_title('$t = 0.25s$', fontsize = 10)\n",
        "    ax.axis('square')\n",
        "    ax.set_xlim([-1.1,1.1])\n",
        "    ax.set_ylim([-1.1,1.1])\n",
        "\n",
        "    ax = plt.subplot(gs1[0, 1])\n",
        "    ax.plot(x,usol.T[50,:], 'b-', linewidth = 2, label = 'Exact')       \n",
        "    ax.plot(x,u_pred.T[50,:], 'r--', linewidth = 2, label = 'Prediction')\n",
        "    ax.set_xlabel('$x$')\n",
        "    ax.set_ylabel('$u(x,t)$')\n",
        "    ax.axis('square')\n",
        "    ax.set_xlim([-1.1,1.1])\n",
        "    ax.set_ylim([-1.1,1.1])\n",
        "    ax.set_title('$t = 0.50s$', fontsize = 10)\n",
        "    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.35), ncol=5, frameon=False)\n",
        "\n",
        "    ax = plt.subplot(gs1[0, 2])\n",
        "    ax.plot(x,usol.T[75,:], 'b-', linewidth = 2, label = 'Exact')       \n",
        "    ax.plot(x,u_pred.T[75,:], 'r--', linewidth = 2, label = 'Prediction')\n",
        "    ax.set_xlabel('$x$')\n",
        "    ax.set_ylabel('$u(x,t)$')\n",
        "    ax.axis('square')\n",
        "    ax.set_xlim([-1.1,1.1])\n",
        "    ax.set_ylim([-1.1,1.1])    \n",
        "    ax.set_title('$t = 0.75s$', fontsize = 10)\n",
        "    \n",
        "    plt.savefig('Burgers-Inverse.png',dpi = 500)   "
      ],
      "metadata": {
        "id": "j7m02X_26hFk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Problem Setup\n",
        "https://github.com/omniscientoctopus/Physics-Informed-Neural-Networks\n",
        "\n",
        "**Burgers' Equation**\n",
        "\n",
        "$$\\frac{\\partial u}{\\partial t}+ \\lambda_1u\\frac{\\partial u}{\\partial x}=\\lambda_2\\frac{\\partial^2 u}{\\partial x^2} $$\n",
        "\n",
        "$$x\\in[-1,1]$$\n",
        "$$t\\in[0,1]$$\n"
      ],
      "metadata": {
        "id": "koofMp-5Y-UA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inverse Problems\n",
        "\n",
        "**Forward Problem**: Model$→$Data (Predict)\n",
        "\n",
        "**Inverse Problem**: Data→Model (i.e., actually, we get the Model's parameters)\n",
        "\n",
        "###Example\n",
        "\n",
        "(See: https://book.sciml.ai/notes/10/)\n",
        "\n",
        "A Neural Network is an example of an inverse problem:\n",
        "\n",
        "1.   We have an unknown model that follows:\n",
        "\n",
        "$$N(X)=W_n\\sigma_{n-1}(W_{n-1}\\sigma_{n-2}(...(W_2(W_1X+b_1)+b_2)+..)+b_{n-1})+b_n$$\n",
        "\n",
        "2.   We use our \"training\" data to get $W_i$ and $b_i$; for $i=1,2,..\\#layers$.\n",
        "\n",
        "\n",
        "Specifically:\n",
        "\n",
        "Data → Model i.e., estimate our model parameters (i,e., $W_i$ and $b_i$)\n",
        "\n"
      ],
      "metadata": {
        "id": "7Q6tIPKwKuD_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# PINNs for Inverse Problems or \"Data-driven Discovery of Nonlinear Partial Differential Equations\"\n",
        "\n",
        "(See: https://arxiv.org/abs/1711.10566)\n",
        "\n",
        "**Inverse Problem**: Data$→$Model's parameters so:\n",
        "\n",
        "Data →PINN→ Model Parameters (i.e., our PDE parameters)\n",
        "\n",
        "For parameterized and nonlinear partial differential equations of the general form (Raissi et al., 2017):\n",
        "\n",
        "$$u_t+\\mathscr{N}[u;\\lambda]=0$$\n",
        "\n",
        "where, $u(x,t)$ is the hidden solution and $\\mathscr{N}[;\\lambda]$ is a nonlinear operator parameterized by $\\lambda$.\n",
        "\n",
        "In short: We will use a PINN to get $\\lambda$.\n",
        "\n",
        "\n",
        "## Analysis:\n",
        "\n",
        "Let:\n",
        "\n",
        "$u_t=\\frac{\\partial u}{\\partial t}$\n",
        "\n",
        "$u_x=\\frac{\\partial u}{\\partial x}$\n",
        "\n",
        "$u_{xx}=\\frac{\\partial^2 u}{\\partial x^2}$\n",
        "\n",
        "$\\mathscr{N}[u;\\lambda]=\\lambda_1u u_x-\\lambda_2 u_{xx}$\n",
        "\n",
        "Our PDE is described as:\n",
        "\n",
        "$$u_t+\\lambda_1uu_x=\\lambda_2u_{xx}$$\n",
        "\n",
        "If we rearrange our PDE, we get:\n",
        "\n",
        "$$u_t+\\lambda_1uu_x-\\lambda_2 u_{xx}=0$$\n",
        "\n",
        "Or,\n",
        "\n",
        "$$u_t+\\mathscr{N}[u;\\lambda]=0$$\n",
        "\n",
        "So we can use a PINN to obtain $\\lambda$!\n",
        "\n",
        "In our case (from the reference solution) $\\lambda=[\\lambda_1,\\lambda_2]=[1,\\nu]=\\left[1,\\frac{1}{100\\pi}\\right]$\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "0WVXB20FOPny"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neural Network\n",
        "\n",
        "A Neural Network is a function (See:https://book.sciml.ai/notes/03/):\n",
        "\n",
        "$$N(X)=W_n\\sigma_{n-1}(W_{n-1}\\sigma_{n-2}(...(W_2(W_1X+b_1)+b_2)+..)+b_{n-1})+b_n$$\n",
        "\n",
        "**Note:**We usually train our N by iteratively minimizing a loss function in the training dataset(known data) to get $W_i$ and $b_i$.\n",
        " \n",
        "## PINNs=Neural Network + PDE \n",
        "(See: https://www.sciencedirect.com/science/article/pii/S0021999118307125)\n",
        "\n",
        "We can use a neural network to approximate any function (Universal APproximation Theorem): (See:https://book.sciml.ai/notes/03/)\n",
        "$$N(x,t)\\approx u(x,t)$$ \n",
        "\n",
        "Since N(x,t) is a function, we can obtain its derivatives: $\\frac{\\partial N}{\\partial t},\\frac{\\partial^2 N}{\\partial x^2}, etc.$.(Automatic Diferentiation) \n",
        "\n",
        "Assume:$$N(t,x)\\approx u(t,x)$$ \n",
        "\n",
        "Then:\n",
        "\n",
        "$$N_t+\\lambda_1NN_x-\\lambda_2 N_{xx}\\approx u_t+uu_x-\\lambda u_{xx}=0$$\n",
        "\n",
        "And:\n",
        "\n",
        "$$N_t+\\lambda_1NN_x-\\lambda_2 N_{xx}\\approx 0$$\n",
        "\n",
        "\n",
        "We define this function as $f$:\n",
        "\n",
        "$$f(t,x)=N_t+\\lambda_1NN_x-\\lambda_2N_{xx}$$\n",
        "\n",
        "Remember our operator:\n",
        "\n",
        "$$f(t,x)=N_t+\\mathscr{N}[N,\\lambda]$$\n",
        "\n",
        "So:\n",
        "\n",
        "$$f(t,x)\\approx 0$$\n",
        "\n",
        "### PINNs' Loss function\n",
        "\n",
        "We evaluate $f$ in a certain number of points ($N_u$) inside our domain $(x,t)$. Then we iteratively minimize a loss function related to $f$:\n",
        "\n",
        "$$MSE_f=\\frac{1}{N_u}\\sum^{N_u}_{i=1}|f(t_u^i,x_u^i)|^2$$\n",
        "\n",
        "Usually, the training data set is a set of points from which we know the answer. In our case, points inside our domain (i.e., interior points). **Remember that this is an inverse problem, so we know the data.**\n",
        "\n",
        "Since we know the outcome, we select $N$ and compute the $MSE_u$** (compare it to the reference solution).\n",
        "\n",
        "$$MSE_{u}=\\frac{1}{N_u}\\sum^{N_u}_{i=1}|u(t_{u}^i,x_u^i)-N(t_{u}^i,x_u^i)|^2$$\n",
        "\n",
        "Please note that $\\{t_u^i,x_u^i\\}_{i=1}^N$ are the same in $MSE_f$ and $MSE_u$\n",
        "\n",
        "#### Total Loss:\n",
        "\n",
        "$$MSE=MSE_{u}+MSE_f$$\n",
        "\n",
        "NOTE: We minimize $MSE$ to obtain the $N$'s parameters (i.e, $W_i$ and $b_i$) and the ODE parameters (i.e., $\\lambda$)→ We will ask our neural network to find our $\\lambda$."
      ],
      "metadata": {
        "id": "TQpmQ1CGKpWJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Neural Network"
      ],
      "metadata": {
        "id": "ZHYqtyYJs3Jv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Deep Neural Network\n",
        "class DNN(nn.Module):\n",
        "    def __init__(self,layers):\n",
        "        super().__init__() #call __init__ from parent class \n",
        "              \n",
        "        'activation function'\n",
        "        self.activation = nn.Tanh()\n",
        "    \n",
        "        'Initialize neural network as a list using nn.Modulelist'  \n",
        "        self.linears = nn.ModuleList([nn.Linear(layers[i], layers[i+1]) for i in range(len(layers)-1)])\n",
        "    \n",
        "        'Xavier Normal Initialization'\n",
        "        for i in range(len(layers)-1):\n",
        "            \n",
        "            nn.init.xavier_normal_(self.linears[i].weight.data, gain=1.0)\n",
        "            \n",
        "            # set biases to zero\n",
        "            nn.init.zeros_(self.linears[i].bias.data)\n",
        "            \n",
        "    'foward pass'\n",
        "    def forward(self,x):\n",
        "              \n",
        "        if torch.is_tensor(x) != True:         \n",
        "            x = torch.from_numpy(x)                \n",
        "        \n",
        "        u_b = torch.from_numpy(ub).float().to(device)\n",
        "        l_b = torch.from_numpy(lb).float().to(device)\n",
        "                      \n",
        "        #preprocessing input \n",
        "        x = (x - l_b)/(u_b - l_b) #feature scaling\n",
        "        \n",
        "        #convert to float\n",
        "        a = x.float()\n",
        "        \n",
        "        for i in range(len(layers)-2):\n",
        "            \n",
        "            z = self.linears[i](a)\n",
        "                        \n",
        "            a = self.activation(z)\n",
        "            \n",
        "        a = self.linears[-1](a)\n",
        "        \n",
        "        return a"
      ],
      "metadata": {
        "id": "zBpYiBoY56z1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialize our parameters\n",
        "\n",
        "Our operator is:\n",
        "\n",
        "$$\\mathscr{N}[u;\\lambda]=\\lambda_1u u_x-\\lambda_2 u_{xx}$$\n",
        "\n",
        "We know that:\n",
        "\n",
        "$$\\lambda_1=1$$\n",
        "\n",
        "$$\\lambda_2=\\nu=\\frac{1}{100\\pi}=0.003183$$"
      ],
      "metadata": {
        "id": "ON_wac6VbHM_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lambda1=2.0\n",
        "lambda2=0.2\n",
        "print(\"Te real 𝜆 = [\", 1.0,nu,\"]. Our initial guess will be 𝜆 _PINN= [\",lambda1,lambda2,\"]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6QU9EisVjq_",
        "outputId": "ee337fd3-beb2-4001-efb0-7a254055781d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Te real 𝜆 = [ 1.0 0.003183098861837907 ]. Our initial guess will be 𝜆 _PINN= [ 2.0 0.2 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  PINN\n",
        "\n",
        "#https://github.com/alexpapados/Physics-Informed-Deep-Learning-Solid-and-Fluid-Mechanics\n",
        "\n",
        "class FCN():\n",
        "    def __init__(self, layers):\n",
        "        self.loss_function = nn.MSELoss(reduction ='mean')\n",
        "        'Initialize iterator'\n",
        "        self.iter = 0\n",
        "        'Initialize our new parameters i.e. 𝜆 (Inverse problem)' \n",
        "        self.lambda1 = torch.tensor([lambda1], requires_grad=True).float().to(device)  \n",
        "        self.lambda2 = torch.tensor([lambda2], requires_grad=True).float().to(device)  \n",
        "        'Register lambda to optimize'\n",
        "        self.lambda1 = nn.Parameter(self.lambda1)\n",
        "        self.lambda2 = nn.Parameter(self.lambda2)\n",
        "        'Call our DNN'\n",
        "        self.dnn = DNN(layers).to(device)\n",
        "        'Register our new parameter'\n",
        "        self.dnn.register_parameter('lambda1', self.lambda1)  \n",
        "        self.dnn.register_parameter('lambda2', self.lambda2)                                                           \n",
        "        'loss function'\n",
        "            \n",
        "    def loss_data(self,x,y):\n",
        "                \n",
        "        loss_u = self.loss_function(self.dnn(x), y)\n",
        "      \n",
        "        return loss_u\n",
        "    \n",
        "    def loss_PDE(self, X_train_Nu):\n",
        "                        \n",
        "        lambda1=self.lambda1\n",
        "\n",
        "        lambda2=self.lambda2\n",
        "\n",
        "        g = X_train_Nu.clone()\n",
        "                        \n",
        "        g.requires_grad = True\n",
        "        \n",
        "        u = self.dnn(g)\n",
        "                \n",
        "        u_x_t = autograd.grad(u,g,torch.ones([X_train_Nu.shape[0], 1]).to(device), retain_graph=True, create_graph=True)[0]\n",
        "                                \n",
        "        u_xx_tt = autograd.grad(u_x_t,g,torch.ones(X_train_Nu.shape).to(device), create_graph=True)[0]\n",
        "                                                            \n",
        "        u_x = u_x_t[:,[0]]\n",
        "        \n",
        "        u_t = u_x_t[:,[1]]\n",
        "        \n",
        "        u_xx = u_xx_tt[:,[0]]\n",
        "                                        \n",
        "        f = u_t + (lambda1)*(self.dnn(g))*(u_x) - (lambda2)*u_xx \n",
        "        \n",
        "        loss_f = self.loss_function(f,f_hat)\n",
        "                \n",
        "        return loss_f\n",
        "    \n",
        "    def loss(self,x,y):\n",
        "\n",
        "        loss_u = self.loss_data(x,y)\n",
        "        loss_f = self.loss_PDE(x)\n",
        "        \n",
        "        loss_val = loss_u + loss_f\n",
        "        \n",
        "        return loss_val\n",
        "     \n",
        "    'callable for optimizer'                                       \n",
        "    def closure(self):\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        loss = self.loss(X_train_Nu, U_train_Nu)\n",
        "        \n",
        "        loss.backward()\n",
        "                \n",
        "        self.iter += 1\n",
        "        \n",
        "        if self.iter % 100 == 0:\n",
        "\n",
        "            error_vec, _ = PINN.test()\n",
        "        \n",
        "            print(\n",
        "                'Relative Error(Test): %.5f , 𝜆_real = [1.0,  %.5f], 𝜆_PINN = [%.5f,  %.5f]' %\n",
        "                (\n",
        "                    error_vec.cpu().detach().numpy(),\n",
        "                    nu,\n",
        "                    self.lambda1.item(),\n",
        "                    self.lambda2.item()\n",
        "                )\n",
        "            )\n",
        "            \n",
        "\n",
        "        return loss        \n",
        "    \n",
        "    'test neural network'\n",
        "    def test(self):\n",
        "                \n",
        "        u_pred = self.dnn(X_true)\n",
        "        \n",
        "        error_vec = torch.linalg.norm((U_true-u_pred),2)/torch.linalg.norm(U_true,2)        # Relative L2 Norm of the error (Vector)\n",
        "        \n",
        "        u_pred = u_pred.cpu().detach().numpy()\n",
        "        \n",
        "        u_pred = np.reshape(u_pred,(x.shape[0],t.shape[0]),order='F')\n",
        "                \n",
        "        return error_vec, u_pred"
      ],
      "metadata": {
        "id": "7EZjlbVMi8w0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualize data"
      ],
      "metadata": {
        "id": "pOe9yRvxjakj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = scipy.io.loadmat('/content/gdrive/MyDrive/ScientificML/Codes/Learning/Data/Burgers.mat') \n",
        "x = data['x']                                   # 256 points between -1 and 1 [256x1]\n",
        "t = data['t']                                   # 100 time points between 0 and 1 [100x1] \n",
        "usol = data['usol']                             # solution of 256x100 grid points\n",
        "\n",
        "X, T = np.meshgrid(x,t)                         # makes 2 arrays X and T such that u(X[i],T[j])=usol[i][j] are a tuple\n",
        "plot3D(torch.from_numpy(x),torch.from_numpy(t),torch.from_numpy(usol)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "go0WGbdx7bXB",
        "outputId": "28448519-bc1e-4493-ed9e-0990a0d3c747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape,t.shape,usol.shape)\n",
        "print(X.shape,T.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRKvROqlK8_i",
        "outputId": "bc1139ef-586e-475d-88f7-aa71a5a0732f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(256, 1) (100, 1) (256, 100)\n",
            "(100, 256) (100, 256)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Data"
      ],
      "metadata": {
        "id": "U66GsxSutJcB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_true = np.hstack((X.flatten()[:,None], T.flatten()[:,None]))\n",
        "\n",
        "# Domain bounds\n",
        "lb = X_true[0]  # [-1. 0.]\n",
        "ub = X_true[-1] # [1.  0.99]\n",
        "U_true = usol.flatten('F')[:,None] #Fortran style (Column Major)"
      ],
      "metadata": {
        "id": "1ZN_Jc-gtR-q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(lb,ub)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rrx8VgLR2AYK",
        "outputId": "ae11fd61-8f0e-473d-beba-fd3cf7c30489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1.  0.] [1.   0.99]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Data"
      ],
      "metadata": {
        "id": "dQxdtgBtsqJj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_points=len(x)*len(t)"
      ],
      "metadata": {
        "id": "78iQF_-HDczZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtain random points for interior\n",
        "id_f = np.random.choice(total_points, N_f, replace=False)# Randomly chosen points for Interior\n",
        "X_train_Nu = X_true[id_f]\n",
        "U_train_Nu= U_true[id_f]"
      ],
      "metadata": {
        "id": "RPkYrBNDEJb2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"We have\",total_points,\"points. We will select\",X_train_Nu.shape[0],\"points to train our model.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MMPz1TlEb-t",
        "outputId": "4820434e-a59d-467d-8514-5bfa2dc80b8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 25600 points. We will select 10000 points to train our model.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Original shapes for X and U:\",X.shape,usol.shape)\n",
        "print(\"Final training data:\",X_train_Nu.shape,U_train_Nu.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySJY_09LzRXz",
        "outputId": "edc9a12f-22bd-466a-8b41-c746bc017583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original shapes for X and U: (100, 256) (256, 100)\n",
            "Final training data: (10000, 2) (10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Neural Network"
      ],
      "metadata": {
        "id": "I82eH55ElVSq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'Convert our arrays to tensors and send them to our GPU'\n",
        "X_train_Nu = torch.from_numpy(X_train_Nu).float().to(device)\n",
        "U_train_Nu = torch.from_numpy(U_train_Nu).float().to(device)\n",
        "X_true = torch.from_numpy(X_true).float().to(device)\n",
        "U_true = torch.from_numpy(U_true).float().to(device)\n",
        "f_hat = torch.zeros(X_train_Nu.shape[0],1).to(device)"
      ],
      "metadata": {
        "id": "8KZvgKcalOVV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "layers = np.array([2,20,20,20,20,20,20,20,20,1]) #8 hidden layers"
      ],
      "metadata": {
        "id": "rKVJPn_yG8cf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PINN = FCN(layers)\n",
        "       \n",
        "PINN\n",
        "\n",
        "'Neural Network Summary'\n",
        "print(PINN)\n",
        "\n",
        "params = list(PINN.dnn.parameters())\n",
        "\n",
        "'''Optimization'''\n",
        "\n",
        "'L-BFGS Optimizer'\n",
        "optimizer = torch.optim.LBFGS(params, lr, \n",
        "                              max_iter = steps, \n",
        "                              max_eval = None, \n",
        "                              tolerance_grad = 1e-11, \n",
        "                              tolerance_change = 1e-11, \n",
        "                              history_size = 100, \n",
        "                              line_search_fn = 'strong_wolfe')\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "optimizer.step(PINN.closure)\n",
        "    \n",
        "    \n",
        "elapsed = time.time() - start_time                \n",
        "print('Training time: %.2f' % (elapsed))\n",
        "\n",
        "\n",
        "''' Model Accuracy ''' \n",
        "error_vec, u_pred = PINN.test()\n",
        "\n",
        "print('Test Error: %.5f'  % (error_vec))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTSJPt_CFwYn",
        "outputId": "4bf1fc11-5515-4c38-b735-fd0c5fc83b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<__main__.FCN object at 0x7fc223290dd0>\n",
            "Relative Error(Test): 0.40038 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.10995,  0.00180]\n",
            "Relative Error(Test): 0.27630 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.10895,  0.00209]\n",
            "Relative Error(Test): 0.25630 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.16387,  0.00214]\n",
            "Relative Error(Test): 0.22366 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.40723,  0.00628]\n",
            "Relative Error(Test): 0.20818 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.48434,  0.00725]\n",
            "Relative Error(Test): 0.19597 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.50651,  0.00540]\n",
            "Relative Error(Test): 0.18228 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.57998,  0.00618]\n",
            "Relative Error(Test): 0.17167 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.58211,  0.00498]\n",
            "Relative Error(Test): 0.16647 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.57235,  0.00419]\n",
            "Relative Error(Test): 0.14306 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.69912,  0.00637]\n",
            "Relative Error(Test): 0.13283 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.71367,  0.00532]\n",
            "Relative Error(Test): 0.12728 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.71497,  0.00591]\n",
            "Relative Error(Test): 0.11118 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.76829,  0.00588]\n",
            "Relative Error(Test): 0.09903 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.80368,  0.00585]\n",
            "Relative Error(Test): 0.09237 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.83473,  0.00627]\n",
            "Relative Error(Test): 0.07990 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.85268,  0.00607]\n",
            "Relative Error(Test): 0.06167 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.89493,  0.00573]\n",
            "Relative Error(Test): 0.05145 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.91670,  0.00545]\n",
            "Relative Error(Test): 0.04776 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.92203,  0.00528]\n",
            "Relative Error(Test): 0.04449 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.92475,  0.00500]\n",
            "Relative Error(Test): 0.03936 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.93257,  0.00453]\n",
            "Relative Error(Test): 0.03681 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.94160,  0.00434]\n",
            "Relative Error(Test): 0.03384 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.95541,  0.00428]\n",
            "Relative Error(Test): 0.03254 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.96380,  0.00428]\n",
            "Relative Error(Test): 0.03089 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.96299,  0.00418]\n",
            "Relative Error(Test): 0.02984 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.95442,  0.00404]\n",
            "Relative Error(Test): 0.03045 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.95439,  0.00402]\n",
            "Relative Error(Test): 0.03020 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.95088,  0.00397]\n",
            "Relative Error(Test): 0.02846 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.94987,  0.00382]\n",
            "Relative Error(Test): 0.02731 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.94982,  0.00372]\n",
            "Relative Error(Test): 0.02699 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.95004,  0.00368]\n",
            "Relative Error(Test): 0.02495 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.95899,  0.00363]\n",
            "Relative Error(Test): 0.02452 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.96183,  0.00362]\n",
            "Relative Error(Test): 0.02398 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.96216,  0.00359]\n",
            "Relative Error(Test): 0.02349 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.96321,  0.00357]\n",
            "Relative Error(Test): 0.02367 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.96188,  0.00356]\n",
            "Relative Error(Test): 0.02369 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.96059,  0.00354]\n",
            "Relative Error(Test): 0.02239 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97090,  0.00352]\n",
            "Relative Error(Test): 0.02180 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97652,  0.00349]\n",
            "Relative Error(Test): 0.02158 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97654,  0.00351]\n",
            "Relative Error(Test): 0.02183 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97591,  0.00350]\n",
            "Relative Error(Test): 0.02142 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97686,  0.00349]\n",
            "Relative Error(Test): 0.02138 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97763,  0.00350]\n",
            "Relative Error(Test): 0.02091 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97899,  0.00350]\n",
            "Relative Error(Test): 0.02088 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97862,  0.00349]\n",
            "Relative Error(Test): 0.02054 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97670,  0.00348]\n",
            "Relative Error(Test): 0.02034 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97593,  0.00346]\n",
            "Relative Error(Test): 0.02032 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97544,  0.00344]\n",
            "Relative Error(Test): 0.01987 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97318,  0.00345]\n",
            "Relative Error(Test): 0.01901 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97823,  0.00346]\n",
            "Relative Error(Test): 0.01847 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.97794,  0.00343]\n",
            "Relative Error(Test): 0.01806 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98002,  0.00343]\n",
            "Relative Error(Test): 0.01763 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98072,  0.00342]\n",
            "Relative Error(Test): 0.01762 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98485,  0.00345]\n",
            "Relative Error(Test): 0.01744 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98363,  0.00349]\n",
            "Relative Error(Test): 0.01757 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98622,  0.00352]\n",
            "Relative Error(Test): 0.01757 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98731,  0.00353]\n",
            "Relative Error(Test): 0.01757 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98538,  0.00354]\n",
            "Relative Error(Test): 0.01740 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98843,  0.00355]\n",
            "Relative Error(Test): 0.01715 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99229,  0.00355]\n",
            "Relative Error(Test): 0.01680 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99079,  0.00354]\n",
            "Relative Error(Test): 0.01685 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98819,  0.00353]\n",
            "Relative Error(Test): 0.01689 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98438,  0.00352]\n",
            "Relative Error(Test): 0.01638 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98818,  0.00351]\n",
            "Relative Error(Test): 0.01627 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98656,  0.00350]\n",
            "Relative Error(Test): 0.01565 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98815,  0.00348]\n",
            "Relative Error(Test): 0.01538 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98639,  0.00346]\n",
            "Relative Error(Test): 0.01517 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98859,  0.00345]\n",
            "Relative Error(Test): 0.01537 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98805,  0.00344]\n",
            "Relative Error(Test): 0.01541 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98618,  0.00344]\n",
            "Relative Error(Test): 0.01515 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98573,  0.00342]\n",
            "Relative Error(Test): 0.01480 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98686,  0.00340]\n",
            "Relative Error(Test): 0.01459 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98707,  0.00340]\n",
            "Relative Error(Test): 0.01453 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98745,  0.00340]\n",
            "Relative Error(Test): 0.01416 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98833,  0.00340]\n",
            "Relative Error(Test): 0.01410 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98917,  0.00341]\n",
            "Relative Error(Test): 0.01379 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99178,  0.00342]\n",
            "Relative Error(Test): 0.01377 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99191,  0.00342]\n",
            "Relative Error(Test): 0.01368 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99197,  0.00341]\n",
            "Relative Error(Test): 0.01340 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99287,  0.00340]\n",
            "Relative Error(Test): 0.01329 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99050,  0.00340]\n",
            "Relative Error(Test): 0.01319 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99205,  0.00340]\n",
            "Relative Error(Test): 0.01284 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99296,  0.00338]\n",
            "Relative Error(Test): 0.01285 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99218,  0.00340]\n",
            "Relative Error(Test): 0.01289 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99187,  0.00340]\n",
            "Relative Error(Test): 0.01290 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99294,  0.00342]\n",
            "Relative Error(Test): 0.01304 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99222,  0.00343]\n",
            "Relative Error(Test): 0.01302 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99234,  0.00343]\n",
            "Relative Error(Test): 0.01303 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99068,  0.00342]\n",
            "Relative Error(Test): 0.01300 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99059,  0.00342]\n",
            "Relative Error(Test): 0.01285 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.98989,  0.00339]\n",
            "Relative Error(Test): 0.01265 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99022,  0.00338]\n",
            "Relative Error(Test): 0.01255 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99213,  0.00340]\n",
            "Relative Error(Test): 0.01223 , 𝜆_real = [1.0,  0.00318], 𝜆_PINN = [0.99545,  0.00341]\n",
            "Training time: 189.68\n",
            "Test Error: 0.01211\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plots"
      ],
      "metadata": {
        "id": "JMrDZJC0LqBg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "solutionplot(u_pred,X_train_Nu.cpu().detach().numpy(),U_train_Nu)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "8yI5eSZP5zD6",
        "outputId": "0e84c256-75c6-47b6-e566-08dfc269b33f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1=X_true[:,0]\n",
        "t1=X_true[:,1]"
      ],
      "metadata": {
        "id": "otgA5_xXqb4S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr_x1=x1.reshape(shape=X.shape).transpose(1,0).detach().cpu()\n",
        "arr_T1=t1.reshape(shape=X.shape).transpose(1,0).detach().cpu()"
      ],
      "metadata": {
        "id": "90-ygJjca2c_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot3D_Matrix(arr_x1,arr_T1,torch.from_numpy(u_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "ituXQapsciqE",
        "outputId": "cd3e2225-56aa-4e10-dd04-555bbf7bf9c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot3D(torch.from_numpy(x),torch.from_numpy(t),torch.from_numpy(usol)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "xarXcLDdeF7X",
        "outputId": "dc89cc73-baee-45e1-ad87-b51b1c8df852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## References:\n"
      ],
      "metadata": {
        "id": "K_21EpoD1fZi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1] Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2017). Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561. http://arxiv.org/pdf/1711.10561v1\n",
        "\n",
        "[2] Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (1907). DeepXDE: A deep learning library for solving differential equations,(2019). URL http://arxiv. org/abs/1907.04502. https://arxiv.org/abs/1907.04502\n",
        "\n",
        "[3] Rackauckas Chris, Introduction to Scientific Machine Learning through Physics-Informed Neural Networks. https://book.sciml.ai/notes/03/\n",
        "\n",
        "[4] Repository: Physics-Informed-Neural-Networks (PINNs).https://github.com/omniscientoctopus/Physics-Informed-Neural-Networks/tree/main/PyTorch/Burgers'%20Equation\n",
        "\n",
        "[5]  Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2017). Physics Informed Deep Learning (part ii): Data-driven Discovery of Nonlinear Partial Differential Equations. arXiv preprint arXiv:1711.10566. https://arxiv.org/abs/1711.10566\n",
        "\n",
        "[6] Repository: PPhysics-Informed Deep Learning and its Application in Computational Solid and Fluid Mechanics.https://github.com/alexpapados/Physics-Informed-Deep-Learning-Solid-and-Fluid-Mechanics.\n",
        "\n"
      ],
      "metadata": {
        "id": "SUFzJJ23ZR2O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "TjPpXx0TPaqu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}
